Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

DOI
Publisher DOI
PubMed ID
37190869
Abstract
The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.
Date Issued
2023-08
Publication Type
Article
Subject(s)
Subjects
benchmarking bottom-up models eddy covariance methane emissions observational constraints top-down models wetland modeling
Language(s)
en
Author(s)
Chang, Kuang-Yu
Riley, William J
Collier, Nathan
McNicol, Gavin
Fluet-Chouinard, Etienne
Knox, Sara H
Delwiche, Kyle B
Jackson, Robert B
Poulter, Benjamin
Saunois, Marielle
Chandra, Naveen
Gedney, Nicola
Ishizawa, Misa
Ito, Akihiko
Kleinen, Thomas
Maggi, Federico
McNorton, Joe
Melton, Joe R
Miller, Paul
Niwa, Yosuke
Pasut, Chiara
Patra, Prabir K
Peng, Changhui
Peng, Sushi
Segers, Arjo
Tian, Hanqin
Tsuruta, Aki
Yao, Yuanzhi
Yin, Yi
Zhang, Wenxin
Zhang, Zhen
Zhu, Qing
Zhu, Qiuan
Zhuang, Qianlai
Additional Credits
Journal
Global Change Biology
Publisher
Blackwell Science
ISSN
1354-1013
Access(Rights)
restricted
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